Prediction of compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators

Geopolymer concrete (GPC) has the potential to replace conventional concrete. But, the mixed proportion of GPC poses several difficulties due to various contributing factors. The design of a reliable prediction model becomes challenging because of the non-linear relationship between the proportion of GPC mix and compressive strength (CS). This study implements a hybrid ensemble machine learning model (HEML) from grey wolf optimized conventional machine learning (CML) models to predict the CS of GPC. An experimental database of 1123 records was compiled from different published research work. The database was used to train and test three optimized CML models namely random forest regressor (RFR), Neural network (NN), and Multivariate regression spline (MARS). Utilizing a meta-learner XGBoost algorithm, the CML models' predicted outputs were trained to develop HEML. Statistical parameters notably R2, Adjusted R2, RMSE, MAE, MAPE, VAF, index, and the Shapiro-Wilk statistical test were used to compare the HEML and CML models' predicted outputs. The HEML model showed better performance in both the training and testing phase than the CML models. Two sensitivity analysis methods were adopted to analyze the impact of various parameters on the compressive strength of geopolymer concrete based on the model with the highest prediction accuracy.